Abstract Society has changed immensely due to the revolution in information technology. The question is how this technology can be useful to man. My interest lies in how to apply radar to traffic. People in general relate radar to military matters while the focus here is on traffic accidents. Accidents happen and therefore police and ambulance need to be on site as fast as possible. This master thesis presents a new design with hardware and software combined in one device. The combination is essential in traffic technology. I designed a new and smart traffic light that can be effective in a traffic system. I design, discuss and examine the radar up to the laboratory stage. It is based on the Doppler effect and has a sound sensor that can identify a police car and also detect vehicle speed. The status of the traffic lights can then be changed to give priority to the police car, based on speed limits and other restrictions. Difficulties as well as advantages with the design are discussed.

While increasing number of vehicles on urban roadways create uncontrolled congestion, connectivity among vehicles, traffic lights and other road-side units provide abundant data that paves avenues for novel smart traffic control mechanisms to mitigate traffic congestion and delays. However, increasingly complex vehicular applications have outpaced the computational capabilities of on-board processing units, therefore requiring novel offloading schemes onto additional resources located by the road-side. Adding connectivity and other computational resources on legacy traffic infrastructure may also introduce security vulnerabilities. To ensure that the timeliness and resource constraints of the vehicles using the roadways as well as the applications being deployed on the traffic infrastructure are met, the transportation systems needs to be more predictable. This dissertation discusses three areas that focus on improving the predictability and performance of the connected traffic infrastructure. Firstly, a holistic traffic control strategy is presented that ensures predictable traffic flow by minimizing traffic delays, accounting for unexpected traffic conditions and ensuring timely emergency vehicle traversal through an urban road network. Secondly, a vehicular edge resource management strategy is discussed that incorporates connected traffic lights data to meet timeliness requirements of the vehicular applications. Finally, security vulnerabilities in existing traffic controllers are studied and countermeasures are provided to ensure predictable traffic flow while thwarting attacks on the traffic infrastructure. / Doctor of Philosophy / Exponentially increasing vehicles especially in urban areas create pollution, delays and uncontrolled traffic congestion. However, improved traffic infrastructure brings connectivity among the vehicles, traffic lights, road-side detectors and other equipment, which can be leveraged to design new and advanced traffic control techniques. The initial work in this dissertation provides a traffic control technique that (i) reduces traffic wait times for the vehicles in urban areas, (ii) ensures safe and quick movements of emergency vehicles even through crowded areas, and (iii) ensures that the traffic keeps moving even under unexpected lane closures or roadblocks. As technology advances, connected vehicles are becoming increasingly automated. This allows the car manufacturers to design novel in-vehicle features where the passengers can now stream media-rich content, play augmented reality (AR)-based games and/or get high definition information about the surroundings on their car's display, while the car is driven through the urban traffic. This is made possible by providing additional computing resources along the road-side that the vehicles can utilize wirelessly to ensure passenger's comfort and improved experience of in-vehicle features. In this dissertation, a technique is provided to manage the computational resources which will allow vehicles (and its passengers) to use multiple features simultaneously. As the traffic infrastructure becomes increasingly inter-connected, it also allows malicious actors to exploit vulnerabilities such as modifying traffic lights, interfering with road-side sensors, etc. This can lead to increased traffic wait times and eventually bring down the traffic network. In the final work, one such vulnerability in traffic infrastructure is studied and mitigating measures are provided so that the traffic keeps moving even when an attack is detected. In all, this dissertation aims to improve safety, security and overall experience of the drivers, passengers and the pedestrians using the connected traffic infrastructure.

Actualmente, Perú tiene la mayor tasa de mortalidad en la región respecto a accidentes de tránsito, alcanzando en Lima más de 50,000 siniestros vehiculares y más de 500 fallecidos al año, según la Policía Nacional del Perú. Esto se debe, principalmente, a la imprudencia de conductores y/o peatones, pero también a la demora en la atención debido a las condiciones del tránsito en la ciudad, lo que aumenta el índice de víctimas fatales. Las entidades locales responsables se encuentran desarrollando proyectos para el mejoramiento del tránsito, no obstante, se ha identificado que las soluciones actuales no cuentan con las características suficientes para poder cubrir los problemas presentados. El presente proyecto tiene como objetivo principal implementar una solución tecnológica basada en Smart Traffic para optimizar la identificación y atención de accidentes de tránsito en Lima Metropolitana, empleando infraestructura vial, tecnológica y soluciones analíticas. Esta solución consiste en la captura de datos a través de diversos dispositivos, el procesamiento y análisis de los datos a través de un motor de analítica, la ejecución de acciones en tiempo real para la detección y atención de los siniestros, y la presentación de resultados y reportes, todo soportado en la infraestructura vial existente y en tecnologías como Internet of Things, Big Data & Analytics y Cloud Computing. Asimismo, se ha realizado la validación del modelo propuesto en base a escenarios de prueba con métricas existentes, y se ha elaborado un plan de continuidad que asegure la viabilidad y alta disponibilidad de la propuesta. / Currently, Peru has the highest mortality rate in the region regarding traffic accidents, reaching more than 50,000 vehicle accidents in Lima and more than 500 deaths per year, according to Policía Nacional del Perú. This is mainly due to the recklessness of drivers and / or pedestrians, but also to the delay in attention due to the current traffic conditions in the city, which increases the rate of fatalities. Local entities in charge are developing projects to improve traffic, however, it has been identified that current solutions do not have sufficient characteristics to cover the problems presented. The main objective of this project is to implement a technological solution based on Smart Traffic to optimize the identification and attention of traffic accidents in Lima Metropolitana, using road infrastructure, technology and analytical solutions. This solution consists of data capture through various devices, data processing and analysis through an analytics engine, the execution of actions in real time for the detection and attention of traffic accidents, and the presentation of results. and reports, all supported by the existing road infrastructure and by technologies such as Internet of Things, Big Data & Analytics and Cloud Computing. Also, the proposed model has been validated based on test scenarios with existing metrics, and a continuity plan has been drawn up to ensure the viability and high availability of the proposal. / Tesis

Los tréboles son infraestructuras diseñadas para mejorar la circulación y seguridad vehicular en las intersecciones. El trébol analizado se define como una intersección vial a desnivel parcial de tres lazos y cuatro ramales directos; el cual, está comprendido por la vía expresa metropolitana Javier Prado y la vía expresa nacional Panamericana. Esta investigación se realizó mediante un análisis de riesgo donde se obtuvo un nivel de riesgo intolerable identificado en los conductores que invaden las vías adyacentes; los cuales, interrumpen el flujo vehicular y pueden ocasionar colisiones. Además, se identificó problemas en su comportamiento operacional, especialmente en los lazos que confluyen con las vías principales. Esto debido al ingreso desordenado de vehículos, inadecuada dosificación vehicular y maniobras peligrosas realizadas por los conductores que tratan de ingresar a la via principal. La presente investigación propone un sistema de semaforización inteligente con la metodología Ramp metering en base al algoritmo ALINEA. El cual, ordena, mediante una dosificación vehicular en tiempo real, el ingreso de los vehículos hacía las vías principales. Además, disminuye la probabilidad de colisiones ya que aplica un control de ingreso adaptativo. La propuesta logra que la velocidad promedio se incremente en 15,8% y que los tiempos de viaje se reduzcan en un máximo de 19.1%. Adicionalmente, existe un leve incremento de 30 Veh/h en el aforo vehicular del trébol. A partir de lo observado, se demuestra que la optimización de parámetros individuales genera una sinergia que influye positivamente en el comportamiento operacional del trébol. / Clovers are infrastructures designed to improve traffic and vehicular safety at intersections. The analyzed cloverleaf is a partially uneven road intersection with three loops and four direct branches located in the Javier Prado highway and the Panamericana highway in the city of Lima. The research will base on a risk analysis with an intolerable level of risk identified in drivers who invaded adjacent roads. Interrupt the flow of vehicles and can cause chain collisions. Also, it was possible to identify problems in their operational behavior, especially in the loops that converge with the main roads. Due to the disorderly entry of vehicles, inadequate vehicle dosing, the formation of lines in cloverleaf loops, increased probability of collisions and dangerous maneuvers carried out by drivers trying to enter the main road. The present research proposes an intelligent traffic light system with the Ramp metering methodology based on the ALINEA algorithm. Which, through real-time vehicle dosing, orders the entry of vehicles onto the main roads. Besides, it reduces the probability of collisions since it applies adaptive entry control. The research achieves an increased by 15.8% in the average speed, and a maximum of 19.1% reduces that travel times. Additionally, there is a slight increase of 30 Veh / h in clover's vehicle capacity. Furthermore, it shows that the optimization of individual parameters generates a synergy that positively influences the operational behavior of the clover. / Tesis